Quadcopter sensor noise and camera noise recording and simulation

ABSTRACT

A method of simulating a quadcopter includes recording camera output for one or more video cameras under constant conditions and subtracting a constant signal from the recorded camera output to obtain a camera noise recording. Simulated camera noise is generated from the camera noise recording and is added to a plurality of simulated camera outputs of a quadcopter simulator to generate noise-added simulated camera outputs. The noise-added simulated camera outputs are sent to an Artificial Intelligence (AI) controller coupled to the quadcopter simulator for the AI controller to use to pilot a simulated quadcopter of the quadcopter simulator.

BACKGROUND

Radio controlled unmanned aircraft (e.g. drones, such as quadcopters)can move at high speed and make rapid changes in direction when remotelypiloted by a skilled user. In drone racing, users race their respectivedrones around a course using remote-controls to maneuver around thecourse (e.g. through gates, around obstacles, etc.). A camera view froma drone may be relayed to a user to allow a First Person View (FPV) sothat the user can see where the drone is going and steer it accordinglyin the manner of a pilot sitting in the cockpit of an aircraft.

A drone may include a flight controller that provides output to motorsand thus controls propeller speed to change thrust. For example, aquadcopter has four motors, each coupled to a corresponding propeller,with propellers mounted to generate thrust substantially in parallel(e.g. their axes of rotation may be substantially parallel). The flightcontroller may change speeds of the motors to change the orientation andvelocity of the drone and the propellers may remain in a fixedorientation (i.e. without changing the angle of thrust with respect tothe quadcopter) and may have fixed-pitch (i.e. propeller pitch may notbe adjustable like a helicopter propeller so that each motor powers acorresponding fixed-pitch propeller in a fixed orientation with respectto a drone chassis). The flight controller may be directed by commandsreceived from the user's remote-control and may generate outputs tomotors to execute the commands.

SUMMARY OF THE DRAWINGS

FIG. 1 is a top view of an example of a course and a drone moving alonga path through the course.

FIG. 2 is simplified representation of some of the components for oneembodiment of a quadcopter.

FIG. 3 shows an example of an autonomous quadcopter.

FIG. 4 shows an example of a method of developing AI code.

FIG. 5 shows an example of components of an AI controller coupled to asimulator.

FIG. 6A shows an example of an AI controller running AI piloting codecoupled to a workstation implementing a quadcopter simulator.

FIG. 6B shows an example of a method operating an AI controller andquadcopter simulator.

FIG. 7 shows an example of AI controller operation.

FIG. 8 shows some components of an example Software Development Kit(SDK).

FIG. 9 shows an example of an Integrated Development Environment (IDE)features.

FIGS. 10A-C show aspects of an example of a quadcopter simulator.

FIG. 11 shows an example method of testing quadcopter components forsimulation.

FIG. 12 shows an example of a test set-up for quadcopter componenttesting and characterization.

FIG. 13 shows an example of a method of obtaining simulated cameranoise.

FIG. 14 shows an example of a method of obtaining simulated sensornoise.

FIG. 15 shows an example of a test set-up for quadcopter component noisemeasurement.

FIG. 16 shows an example of a workstation coupled to an AI controllerfor debugging AI piloting code while AI piloting code interacts with asimulator.

FIG. 17 shows an example of a method of debugging AI piloting code.

FIG. 18 shows an example of an AI controller coupled to a workstation.

FIG. 19 shows an example of an AI controller coupled to live hardware.

FIG. 20 shows an example of an AI controller in an autonomous drone.

FIG. 21 shows an example of a method of operating an autonomous drone.

FIGS. 22A-D show different views of an example of an autonomous drone.

DETAILED DESCRIPTION

The following presents a system and techniques for an autonomous dronesuch as an autonomous quadcopter that includes an ArtificialIntelligence (AI) controller that is separate from a flight controllerand that provides input to the flight controller to pilot the drone. TheAI controller may provide all commands needed by the flight controllerand thus may take the place of a user using a remote-control (e.g. ahuman pilot flying the drone by remote-control). The AI controller mayuse Computer Vision (CV) based on multiple cameras (e.g. six camerasconfigured as three stereoscopic cameras) to pilot the drone based onvisual input from the environment, determining the flightpath in realtime rather than flying along a predetermined flightpath. A droneequipped with such an AI controller may be an autonomous drone that doesnot require human input to fly around a course (e.g. a race course). TheAI controller may be coupled to other drone components (e.g. flightcontroller) through a connector so that the AI controller is removablefrom the drone, allowing the drone to be configured for remote-control(without the AI controller) and for autonomous flight (with the AIcontroller). The drone may also be switchable between autonomous andremote-control modes without physically removing the AI controller (e.g.a remote-control may send a command to change from autonomous mode toremote-control mode during flight).

The AI controller may include modular components including libraries ofroutines that may be used for piloting a drone. An AI controller may beconfigured with a standardized set of modules for operation ofstandardized hardware and different teams may then develop AI code tointeract with such standardized components in competition. For example,different AI code may be used in combination with other modules to racedrones around a racecourse to compare performance of different AI code.In an example, participating teams may be provided with an AI controllerpreloaded with certain standardized modules and a workstation providingan Integrated Development Environment (IDE) for developing AI code. Adrone simulator may be provided for testing AI code and a debugger maybe provided for debugging AI code running on the AI controller while itis coupled to the simulator. An AI controller for use in an autonomousdrone may be loaded with AI code, coupled to such a simulator anddebugger, and may pilot simulated drones around simulated environments(e.g. simulated racecourses) to debug, test, and teach (e.g. by machinelearning) the AI code.

Debugging may include using accurate timestamping (e.g. Precision TimeProtocol (PTP)) on both an AI controller and a drone simulator (e.g.quadcopter simulator) coupled to the AI controller. In case of an errorin AI piloting code (or other event), timestamps may be used to rewindthe drone simulator to (or past) the point where the error occurred (theerror time). A debugger may then step through operations of the dronesimulator and the AI piloting code to identify a source of an AI codeerror. A clock in the AI controller may be synchronized with a clock ina workstation that implements the drone simulator and the debugger sothat timestamping is accurate across all hardware (e.g. using PTPhardware timestamps to achieve synchronization accuracy of less than onemillisecond)

A drone simulator (e.g. quadcopter simulator) for use with an AIcontroller may include certain features to facilitate interaction withthe AI controller. An AI controller may be more sensitive to smalldifferences between simulated behavior and real behavior than a humanpilot. For example, human pilots may compensate for some inaccuracy andmay not even notice some discrepancies that may affect AI code. A dronesimulator may be used to teach an AI controller (e.g. using machinelearning) how to pilot a drone and the accuracy of such a dronesimulator may subsequently affect how AI code pilots a real drone sothat poor simulation may result in poor piloting (potentially includingcrashes or other unwanted consequences). In order to provide an accuratesimulator, actual drone components may be characterized using benchtesting to generate detailed characterization of different componentcharacteristics across their operating ranges (rather than usingsimplified models that may not be as accurate). The test data can beused to generate detailed lookup tables for various characteristicsunder various conditions. Testing may be performed for a significantpopulation of components (e.g. quadcopter motors, propellers, batteries,etc.) over different conditions (e.g. different current, power, RPM,battery charge) to obtain averaged data. The lookup tables may be usedin a quadcopter simulator to provide extremely accurate simulation of aquadcopter that is suitable for providing to an AI controller.

Simulated sensor output and/or simulated camera output from a dronesimulator may be modified to add simulated noise. In some cases, suchsimulated noise may be at a level that would not be noticed, or wouldnot appear significant to a human pilot but may affect an AI pilot.Suitable simulated noise may be found from bench testing sensors and/orcameras. For example, creating constant conditions, recordingsensor/camera output, and subtracting a constant signal to leave a noisecomponent. A statistically significant population of sensors and/orcameras may be tested, and the test results combined (e.g. averaged) toobtain simulated noise that may then be added to simulated sensor outputand/or simulated camera output. Such noise-added outputs may be providedto an AI controller and may be used by the AI controller to pilot asimulated drone.

AIthough the following description is primarily given the context ofdrones (e.g. quadcopters) moving along a three-dimensional path througha course (e.g. a racecourse where drones compete to go around theracecourse and reach a finish line by selecting the fastest flightpath),certain concepts presented can be applied more generally. For example,the systems and techniques can be applied to non-drone aircraft or otherobjects that serve as a mobile source of the described signals as itmoves along a three-dimensional path.

FIG. 1 is a top view of an example of a course and a drone moving alonga path through the course. From the start location, the course passesthrough the gates G1 -G6 111-116 sequentially and then through an endgate EG 117 to arrive at the finish location. The drone 101 is shownmoving along the path through the series of gates. A set of controltransceivers cTx1-4 151-154 cover the region that includes the course tosupply control signals to drones on the course and also receive databack from the drones so that users, using remote-controls, may fly theirdrones and may see video from a camera on their drone (FPV). AIthoughthe start and finish of the course shown FIG. 1A are shown as near eachother, this need not be so in general. Similarly, although the course isshown defined by a series of frame-like gates, pylons or otherstructures can be used to specify a course or path. While drone racingprovides one area in which the present technology may be used, thepresent technology is not limited to racing and may be used to operate avariety of drones and other autonomous craft in a variety ofenvironments.

FIG. 2 is simplified representation of some of the components for oneexample of a drone 201, which is a remote-controlled quadcopter in thisexample. FIG. 2 shows flight controller 211 connected to motors 217 a-d(which turn respective propellers, not shown in this view), the voltagesource and regulator 213, wireless receiver 215, video camera 231 andaltitude sensor 233, and the transmitters 225 and 227. In thisembodiment, extending on an arm from each of the corners of the drone isa motor 217 a-d, each of which is controlled by the flight controller211 to thereby control thrust generated by propellers attached to motors217 a-d. A voltage source (e.g. battery) and regulator 213 suppliespower. A pilot's commands are transmitted from control signaltransceivers such as cTx 223, received by wireless receiver 215. Controlsignal transceiver cTx 223 may be in a remote-control operated by apilot (remote-control user) to fly drone 201 The flight controller 211uses power from the voltage source 213 to drive the motors 217 a-daccording to the pilot's signals.

The drone also includes video camera 231 and altitude sensor 233 thatsupply data to the flight controller 211. An FM or other type videotransmitter 225 transmits data from the video camera 231 to a videomonitor receiver vRx 221 (external to the drone, such as on the ground)that monitors the video signals and passes on the video data to thepilot. Data can also be sent back to the control signal transceiver cTx223 by the transmitter 227. AIthough the transmitter 227 and wirelessreceiver 215 are shown as separate elements in FIG. 2, in manyembodiments these will be part of a single transceiver module (e.g. aremote-control may include both a control signal transceiver and a videomonitor receiver to allow a remote-control user to see video from videocamera 231 while piloting drone 201).

FIG. 3 shows an example of an autonomous drone 301 (autonomousquadcopter in this example), which is different to drone 201 in that itis configured for autonomous operation, instead of, or in addition toreceiving commands from a remote user. For example, autonomous drone 301may fly around a course such as illustrated in FIG. 1, maneuveringthrough gates, around obstacles, etc. without commands from a remoteuser. Instead of receiving commands via RF communication from aremote-control, when in autonomous mode, autonomous drone 301 mayoperate according to commands generated by an Artificial Intelligence(AI) controller 330, which is coupled to the flight controller 211(components of autonomous drone 301 that are common to drone 201 aresimilarly labeled). In this arrangement, AI controller 330 selects aflightpath and generates commands according to the same command set usedby a remote-control. Thus, remote unit 332 may send commands to flightcontroller 211 according to a predetermined command set when autonomousdrone 301 is in a remote-control mode. AI controller 330 may sendcommands to flight controller 211 according to the same predeterminedcommand set when autonomous drone 301 is in an autonomous mode. In thisway, flight controller 211 may operate similarly in both remote-controlmode and autonomous modes and does not require reconfiguration. Thisallows drones developed for remote-control to be easily adapted forautonomous operation, thus taking advantage of preexisting componentsand shortening development time for autonomous quadcopter development.

In an example, AI controller 330 may be implemented in an AI module thatmay be considered as a bolt-on component that may be added to afully-functional drone (e.g. instead of, or in addition to aremote-control). For example, AI controller 330 may be implemented by acontroller module, such as an NVIDIA Jetson AGX Xavier module, whichincludes a Central Processing Unit (CPU), Graphics Processing Unit(GPU), memory (e.g. volatile memory such as DRAM or SRAM), data storage(e.g. non-volatile data storage such as flash), and Vision accelerator.Other suitable controller hardware may also be used. The AI controller330 may be connected to flight controller 211 and other quadcoptercomponents through a physical connector to allow it to beconnected/disconnected for configuration for AI control/remote-control.AI controller 330 may be physically attached to autonomous drone 301 bybeing clipped on, bolted on, or otherwise attached (e.g. to the chassisof drone 301) in a manner that makes physical removal easy.

While a human pilot may fly a drone based on video sent to the pilotfrom the drone, an AI pilot, such as embodied in AI controller 330 maypilot a drone based on different input including sensor input and/orinput from multiple cameras (e.g. using Computer Vision (CV) to identifyand locate features in its environment). While human pilots generallyrely on a single camera to provide a single view (first person view, or“FPV”), an AI pilot may use a plurality of cameras that cover differentareas (e.g. a wider field of view, more than 180 degrees and as much as360 degrees). In an example, cameras may be arranged in pairs, with apair of cameras having overlapping fields of view. This allows such apair of cameras to form a stereoscopic camera so that depth of fieldinformation may be extracted by a CV unit. FIG. 3 illustrates an exampleof camera 334 a and camera 334 b, which are arranged with overlappingfields of view to form a stereoscopic camera 334. Similarly, cameras 336a and 336 b form stereoscopic camera 336 and cameras 338 a and 338 bform stereoscopic camera 338. It will be understood that theorientations (different angles corresponding to different views) andlocations of cameras shown in FIG. 3 are illustrative and that thenumber, location, arrangement, and pairing of such cameras may be variedaccording to requirements (e.g. more than three stereoscopic cameras maybe used). In the example of FIG. 3, video outputs of all cameras, 334 a,334 b, 336 a, 336 b, 338 a, and 338 b (and any other cameras) are sentto AI controller 330. While one or more video output may be transmittedto an external location (e.g. transmitted by transmitter/receiver 340 toremote unit 332), in some cases no such transmission is performed whenautonomous drone 301 is in autonomous mode. In some cases, an autonomousdrone such as autonomous drone 301 is configurable to receive commandsfrom a remote-control such as remote unit 332 (e.g. may beremote-controlled at certain times, e.g. according to selection by aremote user) through a communication circuit. These commands may use thesame command set so that commands from AI controller 330 and remote unit332 are interchangeable. Transmitter/receiver 340 may be considered anexample of a Radio Frequency (RF) communication circuit coupled to theflight controller 211, the RF communication circuit (e.g. RF receiver)is configured to receive external commands from a remote-control (e.g.remote unit 332) and provide the external commands to the flightcontroller 211 to direct the flight controller to follow aremotely-selected flightpath, the external commands and the commandsprovided by the AI controller 330 from a common command set.

AI controller 330 includes computer vision (CV) capability to interpretinput from cameras 334 a, 334 b, 336 a, 336 b, 338 a, and 338 b to gaininformation about the environment around drone 301 (e.g. objectidentification and location). Stereoscopic cameras 334, 336, 338 areconfigured to obtain different stereoscopic views to allow depth offield analysis so that the proximity of objects (including racecoursefeatures such as gates, drones, and other racecourse features) may beaccurately determined. AI controller 330 may use CV capability togenerate a three-dimensional (3-D) picture of the surrounding ofautonomous drone 301, or a portion of the surroundings (e.g. generallyahead of autonomous drone 301 along its direction of travel). In somecases, multiple cameras may be used to collectively provide a full360-degree field of view. In other cases, cameras may cover less than360 degrees but may still collectively cover a larger field of view thana human pilot could effectively monitor. Video output from cameras 334a, 334 b, 336 a, 336 b, 338 a, and 338 b may be directly provided to AIcontroller 330 without conversion to RF and transmission as used byremote-controlled drones (e.g. remote-controlled quadcopters). This mayallow rapid reaction as drone 301 moves and video output reflectschanging surroundings (e.g. reduced latency may allow faster responsethan with remote-control).

AI controller 330 is coupled to the plurality of cameras 334 a, 334 b,336 a, 336 b, 338 a, and 338 b to receive input from the plurality ofcameras, determine a flightpath for the autonomous quadcopter (e.g.drone 301) according to the input from the plurality of cameras, andprovide commands to the flight controller 211 to direct the flightcontroller 211 to follow the flightpath. Thus, the role of flightcontroller 211 is to execute commands from AI controller 330 (as itwould from a remote-control user), while AI controller makes pilotingdecisions based on video input (and, in some cases, other input, e.g.from sensors). AI controller 330 may be considered an example of anArtificial Intelligence (AI) controller coupled to a plurality ofcameras (e.g. cameras 334, 336, 338) to receive input from the pluralityof cameras, determine a flightpath for the autonomous quadcopter 301according to the input from the plurality of cameras, and providecommands to the flight controller 211 to direct the flight controller tofollow the flightpath. Flight controller 211 is coupled to the fourmotors 217 a-d to provide input to the four motors to control flight ofthe autonomous quadcopter 301.

In addition to cameras 334 a, 334 b, 336 a, 336 b, 338 a, and 338 b,autonomous drone 301 includes Inertial Measurement Unit (IMU) sensors342 and rangefinder 344. IMU sensors 342 may measure one or more ofspecific force, angular rate, and magnetic field using a combination ofaccelerometers (acceleration sensors), gyroscopes (gyroscopic sensors),and magnetometers to generate motion data (e.g. autonomous quadcoptermotion data). For example, IMU sensors 342 may be used as a gyroscopeand accelerometer to obtain orientation and acceleration measurements.Rangefinder 344 (which may be considered a distance or range sensor)measures the distance from autonomous drone 301 to an external feature(e.g. the ground, obstacle or gate along a racecourse, etc.) Rangefinder344 may use a laser to determine distance (e.g. pulsed laser, or LightDetection and Ranging “LiDAR”). Outputs from sensors 342 and 344 areprovided to AI controller 330 in this example. Outputs from such sensorsmay also be provided to a flight controller (e.g. flight controller 211)in some cases. In addition to the sensors illustrated, an autonomousdrone may include other sensors such as a barometer, or altimeter, todetermine height of a drone above ground, and/or LIDAR sensors usinglasers to generate 3-D representations of surroundings. In some cases, aGlobal Positioning System (GPS) module may be provided to provideposition information based on communication with GPS satellites.

AI controller 330 may be in the form of a removable module that is addedto a drone to provide capacity for autonomous operation. Within AIcontroller 330, certain modules may be provided with differentfunctions. In an example, different AI technologies may be comparedside-by-side by loading AI controllers with different AI code and flyingdrones using the different AI code (e.g. in a race) to compare AItechnologies. In such an example, certain basic functions of AIcontroller 330 may be provided by standard modules that are common tomultiple AI controllers while other functions may be customized by aparticular module, or modules, that are then compared by flying droneswith identical drone hardware, AI controller hardware, and someidentical modules within AI controllers provide a comparison of AItechnologies without effects of different hardware and/or softwaredifferences unrelated to AI piloting. According to an example,autonomous drone racing uses different AI technologies in identicalautonomous drones. This eliminates hardware differences. Certain commonsoftware may be provided in standard AI controllers to provide a commonplatform (common hardware and software elements) that accommodatesdifferent AI technologies and allows them to compete on an equalfooting. This provides development teams with an opportunity to focus oncore technology, reduces cost, and reduces development time. Racingdrones around complex courses provides comparison between differentcandidate AI technologies and can identify winning candidates forfurther development. This provides valuable information, reduces wastedresources on unpromising technologies, and rapid identification ofwinning technologies reduces overall development time and cost.

FIG. 4 illustrates an example of a method of developing AI technologywhich may be used in an autonomous drone, e.g. used in AI controller 330of autonomous drone 301. The method of FIG. 3 generates AI code inmultiple iterations, with testing of AI code using a simulator (e.g.quadcopter simulator) to test AI code at each iteration. The simulatormay be implemented using a workstation that is coupled to an AIcontroller. The simulator may have some features in common with dronesimulators used by human pilots. However, differences between humancontrolled flight (through remote-control) and autonomous flight mayresult in important differences in such simulators and in how they arecoupled to pilots.

The method of FIG. 4 may be used by multiple teams of AI developers(participants) to develop AI code for flying autonomous drones (e.g. todevelop AI code for AI controller 330). Development of AI code in FIG. 4occurs without the use of actual drones so that, in the case of anyfailure (e.g. crash, collision, etc.), no hardware is destroyed so thatdevelopment costs are not impacted by hardware repair and replacement.Only after the AI code is tested against a simulator is it deployed foruse to fly drones. In some cases, simulation may include some hardwareelements (Hardware-in-the-loop, or HITL testing). However, thisgenerally occurs on a testbench or other test environment where alimited number of components (e.g. cameras, sensors, etc.) may be testedwithout actually flying a drone. Thus, destruction of hardwarecomponents during development is generally avoided or reduced.

In the method of FIG. 4, a software development kit (SDK), or “Dev kit”and workstation are sent to a participant 450 (e.g. to each participantof a group of participants). The SDK may include software components tofacilitate generating AI code. Sample projects may be provided forparticipants to check and become familiar with the system. The SDK maybe implemented using an AI controller (e.g. AI controller 330) that maybe sent with the SDK and may be preloaded with appropriate softwaremodules. The participant then performs hardware setup 452, for example,coupling the AI controller to a workstation and connecting any hardwarecomponents. A self-test routine 454 may be performed to check forhardware problems (e.g. ensure coupling between workstation and AIcontroller). A sample AI code module may be provided as part of the SDK.A participant may modify sample AI code 456 in order to improve thesample code, e.g. by adding additional AI code to the sample AI codeaccording to the participant's technology. The AI code may be writtenand debugged using an Integrated Development Environment (IDE) that isprovided to the participant. This may be a customized IDE based on anavailable IDE such as the Eclipse IDE from the Eclipse Foundation. Theparticipant may then run the AI code against the simulator 458. Forexample, AI code developed by the participant may be loaded into the AIcontroller, which is coupled to a workstation that includes dronesimulation software. The AI controller may interact with the simulatorto simulate flying of a drone. Subsequently, a participant may run theAI code against test bed hardware 460. For example, a test bed mayinclude components such as cameras, sensors, batteries, motors, so thatthe AI code can be verified with some hardware components in addition toa simulator. A participant may then review results, e.g. sensor logs,telemetry, video 464 to evaluate the performance of the AI code. Basedon this review, a determination may be made as to whether the AI code isready 466, e.g. ready to deploy to an actual drone. In general, multipleiterations may be required to produce AI code that is ready to deploy.When a determination is made that the AI code is not ready, adetermination is made as to whether support is needed 468. For example,the results of running AI code against the simulator and/or hardware mayindicate a problem that requires help from the SDK provider and/orhardware provider or other entity. If such support is indicated, thenthe participant may contact support 470. If no such support isindicated, then the participant may further modify sample AI code 456and commence another iteration of modification and testing. When theparticipant generates AI code that is determined to be ready 466, thisAI code may be deployed 472, e.g. by loading it in an AI controller ofan actual drone and using it to autonomously pilot the drone (e.g.racing a real quadcopter, under control of the AI controller, around aracecourse). By running AI code to fly a simulator over multipleiterations in this way, the risk of crashing an actual drone is reducedand thus costs are reduced. Different AI code versions of AI pilotingcode may be generated in different iterations and compared to select anAI code version for deployment (e.g. according to the fastest timepiloting a simulated quadcopter around a simulated quadcopterracecourse).

Some of the hardware and software components used in the process of FIG.4 are illustrated in FIG. 5. For example, a simulator 502 is shown thatincludes simulated sensors 504 (e.g. simulated accelerometer, simulatedgyroscope, simulated rangefinder, etc.) and simulated cameras 506.Simulator 502 may be implemented on a workstation that is configured bysimulator software to simulate a drone such as a quadcopter. Thefeatures of simulator 502 may be customized according to the drone to besimulated and its hardware components. Thus, simulated sensors 504 mayprovide simulation of any sensors to be included in an actual drone.These may include one or more IMU sensors, rangefinder sensors, LIDARsensors, altimeter sensors and/or other sensors. Output from simulatedsensors 504 may be sent in a format that conforms to a standard suitablefor sensor output. For example, simulated sensor output may be sent inUser Datagram Protocol (UDP) format, or other format that may be used byactual sensors of a drone.

Simulated cameras 506 may generate simulated views from multiple camerasof a simulated drone. In general, simulation is adapted to the hardwareto be simulated so that for simulation of a quadcopter with six camerasarranged as three stereoscopic cameras (e.g. stereoscopic cameras 334,336, 338 of quadcopter 301) simulated cameras 506 produce six simulatedcamera outputs. These outputs may be provided in any suitable format.For example, where simulator 502 is implemented as a workstation,High-Definition Multimedia Interface (HDMI) ports may be provided andcamera outputs may be sent in HDMI format through such HDMI ports. AIcontroller 508 also generates inputs to simulator 502, includingcommands to simulator 502 that correspond to changes in droneorientation and/or direction.

FIG. 5 also shows live hardware 510, which includes sensors 512 (e.g.one or more IMU, magnetometer, rangefinder, lidar, altimeter, and/orother sensor(s)) and cameras 514 (e.g. multiple cameras forming one ormore stereoscopic cameras. Live hardware 510 may include additionaldrone hardware components such as motors, batteries, power controllers,etc. Live hardware 510 may be used in conjunction with simulator 502 totest AI controller 508 and, for example, to test AI code (e.g.participant code 520) operating on controller 508. Live hardware 510 maybe operated on a test bench with individual components operatedseparately, or in combination. In some cases, a complete drone may beprovided as live hardware. AI code operating on AI controller 508 may berun against simulator 502 (as shown at step 458 of FIG. 4) andsubsequently run against live hardware 510 (as shown at step 460 of FIG.4). Simulator 502 and live hardware 510 may be interchangeable (e.g.using the same connectors) so that simulator 502 may be disconnectedfrom AI controller 508 prior to connecting live hardware 510.

FIG. 5 illustrates modules of AI controller 508 including hardwareabstraction layer 516 which abstracts input from simulator 502 and livehardware 510 so that the input is available to other components of AIcontroller 508 in abstracted form and other components do not have toperform conversion. For example, hardware abstraction layer 516 mayreceive interleaved camera output (e.g. alternating frames, orinterleaved line-by-line, or otherwise interleaved) from a plurality ofcameras (simulated cameras 506 and/or cameras 514), deinterleave theinput to regenerate frames from individual cameras, and separatelybuffer the frames for different cameras (e.g. buffered in ROM) so that asequence of frames for a given camera may be provided at the correctframe rate (e.g. a predetermined frame rate such as 60Hz) to componentsof AI controller 508 that may require video input. In an example, videooutput from two cameras or simulated cameras (e.g. a pair of camerasforming a stereoscopic camera) that are interleaved may bedeinterleaved, buffered, and made available to modules such as a CVmodule that may then use the video. Hardware abstraction layer 516 mayabstract sensor input (from simulated or real sensors) and buffer sensordata received at speed so that it is presented at the correct frame rate(e.g. by mapping kernel memory to user memory).

AI controller 508 includes different modules to perform differentfunctions. An AI master 518 is responsible for running AI code includingroutines from other components. AI master 518 may also interact withsimulator 502, with a debugger, and may perform other functions that arenot directly involved with piloting a drone. Participant code 520 iscode written or modified by participants. While other components shownin FIG. 5 may be common to all participants, each participant may createtheir own participant code. FIG. 4 showed a method for development ofparticipant code, such as participant code 520, through modification ofparticipant code (sample AI code) in multiple iterations while othermodules remain unchanged from iteration to iteration. AI controller 508includes pre-installed libraries 522 and a helper library 524. Theselibraries may include various libraries of software routines that may beavailable for operation of a drone. For example, pre-installed libraries522 may include various publicly available libraries that participantcode may use while helper library 524 may include routines that arecustomized for drone operation and may be provided by as part of an SDKto make development of participant code simpler and quicker.

FIG. 6A illustrates an example of physical connections betweencomponents of FIG. 5, including coupling of simulator 502 (running onworkstation 628) to AI controller 508. The arrangement of FIG. 6A may beused to implement the method illustrated in FIG. 4. Simulator 502generates six simulated camera outputs that form three simulatedstereoscopic camera outputs by pairing simulated camera outputs fromsimulated cameras that have overlapping fields of view. Outputs from apair of cameras may be sent over an HDMI connection in an interleavedformat or combined in another way. For example, two or morelower-resolution frames may be combined in a single frame, e.g. twoframes of simulated camera output with 1280×720 pixels may be combinedin a single frame of 1920×1200 pixels. FIG. 6A shows three HDMI cables630, 631, 632 from workstation 628. Each HDMI cable 630, 631, 632 may beconnected to a corresponding HDMI port of workstation 628 and may carryinterleaved output of two simulated cameras that form a simulatedstereoscopic camera. HDMI cables 630, 631, 632 are connected to HDMI toMIPI bridge 634, which converts from HDMI format to MIPI CSI (MobileIndustry Processor Interface Camera Serial Interface, e.g. CSI-2 orCSI-3) format (and may also be referred to as an HDMI to MIPIconverter). The interleaved MIPI output of HDMI to MIPI bridge 634 maybe deinterleaved by a hardware abstraction layer in AI controller 508(e.g. by HAL 516). While FIG. 6A shows HDMI to MIPI bridge 634 as asingle entity, this may be implemented by any suitable hardware. Forexample, three separate bridges may be used to form HDMI to MIPI bridge634, with each such bridge converting one HDMI output to a correspondingMIPI output.

In addition to HDMI and MIPI cables to carry camera data from simulator502 in workstation 628 to AI controller 508, FIG. 6A shows a coupling636 to carry simulated sensor data from simulator 502 to AI controller508. Simulated sensor data may be sent over coupling 636 in the form ofUDP packets to provide simple low-latency communication suitable forsensor data. Simulated sensor data may be packetized in UDP packets,which are time stamped, and have redundancy code added (e.g. calculationand addition of a Cyclic Redundancy Code such as CRC16) and streamed toAI controller 508. FIG. 6A also shows coupling 637 to carry commandsfrom AI controller 508 to simulator 502. For example, such commands mayuse a command set that is suitable for drone operation, such as acommand set that may be used for remote-control of a drone, e.g. acommand set according to the MAVLink protocol, or a MAVLink-like commandset. FIG. 6A also shows coupling 638, which is configured to conveylogging, debugging, and control data between AI controller 508 andworkstation 628 where a debugger 629 may perform debugging of AI codeoperation in AI Controller 508 as AI Controller 508 pilots a simulateddrone of simulator 502. Logging data may include logs of commands andvariables that may be sent from AI controller 508 to debugger 629.Debugging data may include data regarding the state of AI controller 508at a breakpoint (e.g. variable values), timing information, and otherdata that may be useful to debugger 629. Control data may includecommands from debugger to AI code and/or debug-related code operating onAI controller 508. Coupling 637 to carry commands and coupling 638 tocarry logging, debugging and control data may be implemented usingTransfer Control Protocol (TCP), which is a connection-oriented protocolto ensure that a connection is established, and that commands and othercommunication are received. While couplings 636, 637, 638 may beimplemented in any suitable manner, in the present example thesecouplings are implemented through an Ethernet connection 640 so that asingle physical connection may be used for these different purposes.Simulator 502 may include an Ethernet port that is dedicated tocommunication with AI controller 508 for this purpose.

Simulator 502 is also connected to network 642 by coupling 644. Forexample, coupling 644 may be an Ethernet connection and network 642 maybe an intranet or the Internet. Thus, simulator 502 may include anEthernet port for communication with external entities in addition to anEthernet port dedicated to communication with AI controller 508. Inorder to avoid conflicts, a dedicated IP address range may be reservedfor communication between simulator 502 and AI controller 508. Forexample, the IP address range 100.64.0.0/16, which is within a privateIP address range may be used so that no conflict occurs. Simulator 502may be assigned IP address 100.64.0.1 and AI controller may be assignedIP address 100.64.1.1.

FIG. 6A may be considered an example of a quadcopter simulator 502configured to receive quadcopter flight control commands and to generatesimulated sensor output and simulated camera output for a plurality ofstereoscopic cameras of a simulated quadcopter, and an ArtificialIntelligence (AI) controller 508 coupled to the quadcopter simulator,the AI controller configured to receive the simulated sensor output andthe simulated camera output for the plurality of stereoscopic camerasfrom the quadcopter simulator, determine a flightpath for the simulatedquadcopter according to the simulated sensor output and the simulatedcamera output, generate the quadcopter flight control commands accordingto the flightpath, and provide the quadcopter flight control commands tothe quadcopter simulator.

FIG. 6B shows an example of a method that includes generating, in aquadcopter simulator, a plurality of simulated stereoscopic camera views(with different orientations) of a simulated environment around asimulated quadcopter having a position and orientation in the simulatedenvironment 650, sending the plurality of simulated stereoscopic cameraviews to a quadcopter Artificial Intelligence (AI) controller that isconfigured to autonomously pilot a simulated quadcopter according to theplurality of simulated stereoscopic camera views 652, determining, bythe AI controller, a flight path for the simulated quadcopter accordingto the plurality of simulated stereoscopic camera views 654, andgenerating, in the AI controller, a plurality of flight control commandsto pilot the simulated quadcopter along the flight path 656. The methodfurther includes sending the plurality of flight control commands to thequadcopter simulator 658 and in the quadcopter simulator, simulatingexecution of the plurality of flight control commands to obtain updatedposition and orientation for the simulated quadcopter and repeatinggenerating of simulated stereoscopic camera views for the updatedposition and orientation in the simulated environment 660.

FIG. 7 illustrates operation of components of AI controller 508 shown inFIG. 5 when coupled to a simulator to pilot a simulated drone as shownin FIG. 6, or when piloting an actual drone (e.g. when AI controller 508is coupled to a flight controller of a drone such as flight controller211 of drone 301). AI controller 508 may be a suitable controller suchas the NVIDIA Jetson AGX Xavier, which includes CPU cores, GPU cores,deep learning accelerators (DLAs), and a programmable vision accelerator(PVA) suitable for computer vision applications (e.g. may form acomputer vision unit) and machine learning. AI master 518 handles out ofband communication with the IDE, e.g. communication with debugger 629operating on workstation 628 and AI master 518 runs loops for hardwareabstraction layer 516 and participant code 520.

Data flows into hardware abstraction layer 516 (HAL) from either actualsensors (on a drone or test bench) or from a simulator such as simulator502 (over UDP). The HAL 516 provides a common interface for the usercode across any implementation (e.g. with a simulator such as simulator502, with hardware components provided with the development kit or othertest bench hardware, and with hardware of an actual drone) byabstracting sensor data (e.g. gyroscope, accelerometer, magnetometer,rangefinder) into abstract base classes. HAL 516 also abstracts camerafeeds to generate abstracted input for computer vision. Participant code520 reaches into HAL 516 for all communications, including incomingcommunications from sensors and cameras and outgoing communications to aflight controller. Communication from participant code 520 to dronehardware (and simulated hardware) passes through HAL 516 so thatparticipant code 520 can provide commands at a high level and HAL 516then converts these commands to lower-level commands, e.g. providingcommands to a flight controller in terms of thrust, pitch, roll, and yawaccording to a command protocol such as MAVLink-like protocol, or othersuch protocol.

Participant code 520 uses process scheduler 750 to run in either fixedfrequency loops, with data synchronized, or can be scheduled to run acallback at an arbitrary time. Participant code 520 may call routinesfrom helper library 524, which may call (or include) routines frompre-installed libraries 522, including NVIDIA libraries 752 (e.g.VisionWorks, CUDA, CUDA Deep Neural Network library (cuDNN)), OpenCVlibraries 754 (e.g. CUDA accelerated), and TensorFlow libraries 756.Participant code 520 may also directly call routines from pre-installedlibraries 522. Helper library 524 has a common interface across allplatforms (e.g. simulator, test bench hardware, actual drone hardware)and may include a light wrapper for some common computer vision tasks,algorithms, and data structures, as well as control of the drone, whichgoes through HAL 516.

Further details of software components that may be included inpre-installed libraries 522 are illustrated in FIG. 8. FIG. 8 showsNVIDIA libraries 752 including NVIDIA VisionWorks 860 (a softwaredevelopment package for computer vision and image processing, which mayform part of a computer vision unit), Deep Learning SDK 862 (tools andlibraries for designing GPU-accelerated deep learning applications),TensorRT 864 (a platform for deep-learning inference), cuDNN 866 (aGPU-accelerated library of primitives for deep neural networks), andCUDA toolkit 868 (a development environment for creatinghigh-performance GPU-accelerated applications). FIG. 8 also shows OpenCV754 (an open source computer vision library, which may be considered aCV unit, or part of a CV unit), TensorFlow 756 (an open source softwarelibrary for high performance machine learning and numericalcomputation), Point Cloud Library 870 (an open-source library ofalgorithms for point cloud processing tasks and 3D geometry processing,e.g. for 3D computer vision), SLAM libraries 872 (libraries related toSimultaneous Localization And Mapping, or “SLAM” e.g. ORB-SLAM2, YOLO,etc.), Object detectors 874 (this may include elements in addition toobject detection elements in OpenCV 754 and TensorFlow 756 libraries).In addition to pre-installed libraries 522, an SDK 876 that is providedto participants may include drone navigation Application ProgrammingInterface (API) 878 to facilitate development of participant code andmachine learning training sets 880 that may include training sets thatare specific to features to be encountered by drones (e.g. racecoursecomponents such as gates, obstacles, and other drones for racingdrones). These may be used by machine learning code (e.g. in participantcode 520) so that such code learns at least some basic piloting fromsuch training sets.

FIG. 9 illustrates some features of an Integrated DevelopmentEnvironment (IDE) 984 that may be used to develop AI code such asparticipant code 520. IDE 984 may include elements of SDK 876 shown inFIG. 8, with additional elements, and may be provided to participantsfor use in a development process as illustrated in FIG. 4. This providesa large number of features to a participant so that the participant doesnot have to develop these features and can focus on AI code for flying adrone. The IDE provides a participant with IDE features 984 includinggeneral IDE features 986, which include code assistance 988. Codeassistance 988 includes features such as syntax highlighting 990(allowing source code to be displayed in different colors and/or fontsaccording to the category of code terms), code completion 992 (e.g.providing popups querying parameters of functions and identifying syntaxerrors) and jump to documentation 994 (allowing a direct jump from aneditor to related documentation). General IDE features 986 also includedebugging 996, which includes features such as set breakpoints 998(allowing a user to set breakpoints in AI code for debugging purposes)and inspect variables 902 (allowing a user to get variable values at abreakpoint). General IDE features 986 also includeprofiling/instrumentation features 904, such as run-time metrics 906(showing metrics such as load and memory usage) and CPU/GPU usage 908(displaying CPU/GPU usage to user). General IDE features 986 alsoinclude Deploy features 910, such as deploy to simulator features 912(to facilitate deployment to a simulator such as simulator 502implemented in workstation 628) and deploy to test bed features 914 (tofacilitate deployment to a test bed with hardware components, e.g. livehardware 510).

In addition to General IDE features 986, IDE features 984 include dataacquisition/analysis features 916, such as Sensor/camera features 917and MATLAB-like plotting and charting via Python & Matplotlib 922.Sensor/camera features 917 include import data from simulator 918 (toimport simulated sensor/camera from a simulator, such as simulator 502,for use by an AI controller running AI code) and import data from testbed 920 (to import data from actual sensors/cameras on a test bench,such as live hardware 510, for use by an AI controller running AI code).MATLAB-like plotting and charting via Python & Matplotlib 922 includesfeatures such as Plot point clouds 924, plot sensor readings 926, andplot position and derivatives 928 (to plot these elements to providegraphical illustration for users). MATLAB-like plotting and charting viaPython & Matplotlib 922 also includes the feature view SLAM maps 930 toallow a user to view maps related to location and mapping.

IDE features 984 also include Drone Programming Assists 930, includingfeatures such as AI building blocks 932 and Navigation 942. AI BuildingBlocks 932 includes Kalman filters 934 (to estimate unknown variablesfrom statistical data), SLAM algorithms 936 (algorithms related tolocalization and mapping), drone detection 938 (to detect other dronesfor collision-avoidance or other purposes), and ArUco Mapping 940 (alibrary for Augmented Reality (AR) applications based on OpenCV).Navigation 942 features include vector-based features 944 (includingconversion of navigation data to SBUS protocol), manual SBUS 946, andbasic commands 948 (a set of commands that may be MAVLink commands orMAVLink-like so that a common command set is used between AI controllersand simulators, test bench hardware, and live drone hardware). It shouldbe noted that the features illustrated in the example of FIG. 9 is notindented to be exhaustive and that an actual IDE may provide additionalfeatures, or may omit one or more of the features shown. Furthermore,participants may choose which features to use and how to use them.

IDE features 984 shown provide the user with various capabilitiesincluding the following:

-   -   Produce an AI module which will let the user execute code in one        of several threads, in either C or Python (may not be 100%        parity between Python and C, but definitely useful for        prototyping).    -   Output of the build is a shared object which gets loaded by the        master AI process and executed sequentially.    -   Interact with the simulator.    -   Debug their code.

It may do this, for example, by using Eclipse IDE as a basis to providea strong IDE, and developing plugins which handle communications withthe simulator, custom project types, built in help where possible,debugging via GDB, logging, and import/export of flight output, e.g. toa Jupyter notebook, or as raw data. The IDE may leverage Eclipse C/C++and Pydev to provide a unified experience in both languages.Communication between the AI controller and the simulator may be via anasynchronous messaging library, such as ZeroMQ, keeping timesynchronized between the simulator and AI code.

Simulator

FIG. 10A illustrates some aspects of simulator 502 and its operation.FIG. 10 shows simulator 502 coupled to AI controller 508 to simulate adrone (e.g. quadcopter) piloted by AI code on AI controller 508. Threemodules of simulator 502 include the flight controller 1002, virtualpowertrain 1004 (shown in detail in FIG. 10B), and aerodynamic model1006. AI code running on AI controller 508 generates input signals 1008,including commands, which may be the same as commands used forremote-control of a drone. For example, the same command set may be used(e.g. MAVLink, or MAVLink-like commands). Commands may include four axissignals (thrust, yaw, pitch, and roll) in UDP format.

Input signals 1008 are sent to flight controller 1002, which may use thesame flight controller code used in an actual drone (e.g. may use thesame code as flight controller 211). Flight controller 1002 includes aPID loop module 1010 that receives input signals 1008 from AI controller508 and simulated sensor data 1012 from aerodynamic model 1006 (realsensor data would be used in real drone) and uses rate curves 1014 tocalculate outputs to execute the commands received in input signals1008. Rate curves 1014 may include separate rate curves for thrust, yaw,pitch, and roll, and may reflect rate of change in degrees per second.PID loop module 1010 determines differences between desired rotationalposition and current rotational position and differences in a previouscycle and applies PID tune parameters to generate control signals. Thismay occur separately for pitch, roll, and yaw to generate controlsignals accordingly. These outputs are sent to motor mixer/balancer1016, which generates Electronic Speed Control (ESC) outputs 1018,corresponding to the four motors of a quadcopter. Where flightcontroller 1002 is implemented as a flight controller of an actualdrone, these outputs control the motors to fly the drone. Where flightcontroller 1002 is implemented as a flight controller of simulator 502as shown, ESC outputs are provided to virtual powertrain 1004. Thus,Flight controller 1002 is configured to perform Proportional IntegralDerivative (PID) control of quadcopter motors to generate flightcontroller output signals corresponding to four simulated quadcoptermotors of a simulated quadcopter.

Virtual powertrain 1004 (powertrain module) receives ESC output 1018 asfour input signals corresponding to four quadcopter motors, with eachinput signal handled independently on each frame of simulation. Virtualpowertrain 1004 calculates torque and thrust from ESC output 1018according to characterization data of drone components (described inmore detail below). Precise characterization of hardware componentsallows virtual powertrain 1004 to closely model real hardware (i.e.powertrain of an actual quadcopter) and to accurately calculate torqueand thrust values that are generated in a real drone over a range ofconditions including dynamic conditions (e.g. accounting for droneorientation and velocity in any thrust calculation). Signals 1020representing torque (e.g. in Newton-meters “Nm) and thrust (e.g. inNewtons) are sent from virtual powertrain 1004 to aerodynamic model1006. Virtual powertrain 1004 is configured to generate thrust andtorque values for the four simulated quadcopter motors from the flightcontroller output signals 1018 using a plurality of lookup tables withentries indicating quadcopter component characteristics in an accuratemanner.

Aerodynamic model 1006 (aerodynamic module) is based on a calculation ofa center of mass of a drone by adding up individual weight contributionsof drone components and their distribution. Simulated thrust for eachindividual motor (e.g. each of four motors of a quadcopter) is appliedat the motor attachment point 1022 (i.e. away from the center of mass ofthe drone). Depending on the balance of individual thrust contributions,combined thrust may generate some rotational forces (e.g. pitch androll) about the center of mass of the drone. Accordingly, torque foreach motor is applied about the center of gravity 1024. Thesecalculations are used to calculate resulting combined force and angularmomentum 1026 produced on the drone. If there is a hard surface belowthe drone that is substantially parallel to the plane of the propellers,then the resulting thrust is amplified according to the inverse squareof the distance to that surface producing “ground effect.” Force andangular momentum are corrected for ground effect by ground effectcorrection 1028, which may be in the form of a lookup table based onsimulated height (e.g. output of a simulated altimeter or other heightsensor). Gravity effects 1030 are also applied and a drag model 1032 isapplied to account for drag forces for different drone surfaces (e.g.top, sides) based on different drag coefficients for different surfaces.Drag model 1032 may result in both drag forces (opposite to thedirection of drone movement) and lift forces (perpendicular to thedirection of drone movement) depending on drone orientation. Drag andlift forces may be applied at the center of mass according to dragfactors based on drone surface area and average drag coefficients.Aerodynamic module 1006 is coupled to receive the thrust and torquevalues 1020 from the virtual powertrain 1004, the combine the thrust andtorque values for the four simulated quadcopter motors and applyadditional factors (e.g. ground effect from proximity of ground below,gravity, lift and drag from movement through air) to obtain velocity,angular velocity, and resulting position/rotation 1036 of the simulatedquadcopter, and to provide simulated sensor data 1012 according to thevelocity, angular velocity, and position to the flight controller 1002.

Combined force (thrust) for all motors applied at the center of mass,compensated for ground effect, gravity, drag, lift, and any othersignificant factors, is used to calculate change in drone velocity(change from prior drone velocity), while combined torque for all motorsis used to calculate change in angular velocity. Thus, velocity andangular velocity are calculated and from these values the updatedposition and rotational position (angular orientation) may becalculated. Velocity, angular velocity and resulting position/rotation1036 may be used by sensor simulator 1037 to generate simulated sensordata 1012, which is fed back to flight controller 1002. This feedbackmay be provided at a suitable frame rate or frequency (e.g. 60 Hz).Sensor simulator 1037 may simulate output from simulated sensorscorresponding to drone sensors such as IMU sensors (e.g. includinggyroscope and/or accelerometer components).

In addition to providing simulated sensor data 1012 back to flightcontroller 1002, for use by PID loop 1010, velocity, angular velocityand resulting position/rotation 1036 may be used by camera viewsimulator 1038 (camera simulator) to generate simulated camera viewrenderings. For example, a simulated environment may include simulatedground, walls, gates, drones, and/or other stationary and/or movingobjects that are then simulated in camera renderings according to theposition/rotation of the simulated drone. In one example, camera viewsimulator 1038 generates six simulated camera views that are paired toform three simulated stereoscopic camera views that cover differentareas of a simulated environment around a simulated drone. Camera viewrenderings, along with raw simulated sensor data are provided to AIcontroller 508, which may then select a pathway for the drone based onthe simulated camera views and simulated sensor data. Thus, AIcontroller 508 “sees” the simulated environment around the simulateddrone and receives additional simulated sensor data and can pilot thesimulated drone accordingly by generating commands to control thesimulated drone (input signals 1008).

FIG. 10B shows an example implementation of virtual powertrain 1004 thatprovides accurate drone simulation based on bench testing of hardware toaccurately characterize drone components. While human pilots using droneusing a drone simulator may compensate for inaccuracy in simulatoroutput, an AI pilot may be more sensitive in some cases and accuratesimulation may be beneficial. For example, using a simple equation tomodel behavior of a component may fail to account for some real effects(e.g. turbulence affecting propeller efficiency). Where a simulator isused to train an AI controller (e.g. providing a training set formachine learning) inaccuracy in simulator output may result in an AIcontroller that is not well adapted for flying a real drone. Accordingto an example presented here, accurate characterization of hardwarecomponents is performed and used to generate lookup tables that arequick and easy to access and can be incorporated into a simulator.

FIG. 10B illustrates handling of a single ESC input, corresponding to asingle motor, used to generate torque and thrust by virtual powertrain1004. It will be understood that each such ESC input is separatelyhandled in a similar manner in virtual powertrain 1004 with theresulting torque and thrust components combined in Aerodynamic model1006. ESC output 1018 from flight controller 1002 provides an ESC inputto a current lookup table, current LUT 1042. Current LUT 1042 relatesESC inputs with electrical current based on bench testing of a motor.For example, current LUT 1042 may include a number of entries reflectingaverage current generated in response to a given ESC input duringtesting of a significant number of motors (e.g. in excess of 20) overtheir operational range (e.g. collecting data at 10, 20, or more datapoints spanning the current range of a motor). Thus, output 1044 ofCurrent LUT 1042 is a current (e.g. expressed in Amps) obtained from anentry corresponding to the ESC input to provide current for differentcommand inputs. Output 1044 is provided to a power lookup table—PowerLUT 1046. Power LUT 1046 is a lookup table that relates current withpower (e.g. from Amps to Watts) based on bench testing of a significantnumber of motors over their operational range (e.g. from zero Amps to amaximum current). Output 1048 of power LUT 1046 is a power (e.g. inWatts), which is provided to Battery adjustment unit 1050. Batteryadjustment unit 1050 uses data related to battery condition (e.g. due tointernal resistance, charge level, and/or other factors) as a functionof power and may reflect a reduction in power due to such factors sothat output 1052 may be a reduced power to account for such effects.Battery adjustment unit 1050 may include one or more lookup tableobtained from bench testing of a significant number of batteries overtheir operational range and under different conditions (e.g. differentoutput currents, different levels of charge, etc.) Output 1052 isprovided to Torque LUT 1054 and RPM LUT 1056. Torque LUT 1054 includesentries providing motor torque at different power so that torque output1058 is a torque value corresponding to a torque generated in a motor ofa drone. RPM LUT 1056 is a lookup table that includes entries providingRevolutions Per Minute (RPM) of a motor at different power so thatoutput 1060 is an RPM estimate. Both Torque LUT and RPM LUT may be basedon testing of a significant number of motors across their operationalrange e.g. from zero RPM to a maximum RPM, from zero torque to a maximumtorque. Output 1060 is provided to Delay LUT 1062, which includesentries that reflect delay in increasing RPMs under different conditions(e.g. different starting RPM) found from bench testing. Delay LUT 1062applies the corresponding delay or delays to provide output 1064, whichchanges RPM compared at an appropriate rate to reflect actual motorresponse. Output 1064 is sent to RPM reduction unit 1066, whichcalculates propeller rotation speed limits based on propeller tip speedand propeller drag from current RPM and drone speed and reduces RPMaccordingly. Thus, output 1068 of RPM reduction unit 1066 may be areduced RPM compared with output 1064 under some conditions. This maycombine propeller speed (in RPM) with drone velocity to dynamicallyadjust maximum RPM. Output 1068 is provided to Thrust LUT 1070 andPropeller LUT 1072. Thrust LUT contains entries relating motor staticthrust to motor RPM based bench testing of a significant number ofmotors. Thus, Thrust LUT 1070 generates output 1074, which is a staticthrust value. Propeller LUT 1072 contains entries relating propellerefficiency and RPM based on bench testing a significant number ofpropellers and may reflect effects such as turbulence that may not beaccounted for in a simple propeller model. Thus, Propeller LUT 1072generates output 1076, which is a propeller efficiency. Outputs 1074 and1076 are provided to dynamic thrust unit 1078, which calculates dynamicthrust values from combining static thrust and propeller efficiency togenerate dynamic thrust output 1080 accordingly. Dynamic thrust output1080 from dynamic thrust unit 1078 and torque output 1058 from TorqueLUT 1054 are then sent to aerodynamic model 1006 as combined output 1020as previously shown.

FIG. 10C shows steps performed by virtual powertrain 1004 to accuratelyconvert an ESC input to dynamic thrust and torque outputs using lookuptables. The method shown includes the steps of converting ESC to current1082 (e.g. using Current LUT 1042), converting the current to power 1084(e.g. using Power LUT 1046), and adjusting for battery characteristics1086 (e.g. using Battery adjustment unit 1050) to give an accurate powercorresponding to the ESC signal received. This method further includesconverting this power to RPM 1088 (e.g. using RPM LUT 1056), delayingRPM 1090 to reflect time for motor RPM to change (e.g. using Delay LUT1062), and reducing RPM 1092 according to an RPM limit based on dynamicfactors as needed (e.g. according to drone velocity). The delayed (andreduced, if needed) RPM is then used for obtaining static thrust 1094(e.g. from Thrust LUT 1070) and obtaining propeller efficiency 1096(e.g. from Propeller LUT 1072), which are then used for calculatingdynamic thrust 1098. Calculated dynamic thrust is then provided asoutput to the aerodynamics simulator. Power, after adjusting for batterycharacteristics 1086, is also converted to torque 1099 and the torquevalue is provided to the aerodynamics simulator.

FIG. 11 illustrates a method of quadcopter simulation that includesgenerating and using lookup tables. In particular, FIG. 11 shows amethod of quadcopter simulation that includes testing a plurality ofquadcopter components (e.g. quadcopter motors, quadcopter propellers, orquadcopter batteries) at a plurality of operating conditions acrosstheir operating ranges (e.g. testing quadcopter motors from zerorevolutions per minute to a maximum number of revolutions per minute) toobtain test data 1100, generating one or more lookup tables forcharacteristics of the plurality of quadcopter components at theplurality of operating conditions from the test data 1102, and storingthe one or more lookup tables for quadcopter component simulation 1104.The method further includes, subsequently, in a quadcopter simulator, inresponse to input from a flight controller, receiving a simulated inputvalue for a simulated quadcopter component 1106, reading one or moreentries corresponding to the simulated input value from the one or morelookup tables 1108, generating a simulated quadcopter component outputfrom the one or more entries 1110, and generating a simulated quadcopteroutput to the flight controller according to the simulated quadcoptercomponent output from the one or more entries 1112. Testing may includesending commands to quadcopter motors to rapidly change a number ofRevolutions Per Minute (RPM), recording observed delay in actual changein RPM, generating the one or more lookup tables may include generatinga delay lookup table from the observed delay.

In order to have accurate lookup tables for virtual powertrain 1004, asignificant number of drone components may be tested over a range ofconditions. FIG. 12 illustrates test bench data collection tocharacterize components of a quadcopter (e.g. steps 1100 and 1102 ofFIG. 11) that includes a quadcopter motor 1200 coupled to a propeller1202 so that the quadcopter motor 1200 can spin the propeller 1202 understatic conditions. A power source 1204 (e.g. battery with powercontroller) is coupled to quadcopter motor 1200 to provide electricalcurrent in a controlled manner. A PC 1206 is connected to quadcoptermotor 1200 and may be coupled to power source 1204 (e.g. to controlpower source 1204 to deliver a controlled current). PC 1206 may becoupled to sensors in or near quadcopter motor 1200 to monitorcharacteristics of quadcopter motor 1200 while it spins propeller 1202.For example, PC 1206 may record RPM values for quadcopter motor 1200with different current provided by power source 1204. PC 1206 may recordpower values for different current provided. PC 1206 may record delay inchange in RPM for quadcopter motor 1200 after a changed RPM setpoint isprovided (e.g. providing a step function in requested RPM and measuringhow long it takes actual RPM to reach the new set point). Such data maybe gathered over the entire operating range of motor 1200 (e.g. fromzero RPM to a maximum RPM), for example, at ten or more points between.Testing may be repeated for a statistically significant number ofquadcopter motors and the results may be combined to generate lookuptables (e.g. by averaging). Similarly, a statistically significantnumber of propellers may be tested, and a statistically significantnumber of batteries may be tested, and associated lookup tables may bebased on combining the test results. Batteries may be tested atdifferent levels of charge, different current outputs, etc. Propellersmay be tested at different RPM, proximity to ground (or similarsurface). The apparatus shown in FIG. 12 may be used to generate lookuptables of a virtual powertrain (e.g. lookup tables 1042, 1046, 1054,1056, 1062, 1070, 1072 of virtual powertrain 1004 of FIG. 10B and anyother such lookup tables).

In general, a drone simulator (e.g. a quadcopter simulator) may provideoutputs to a pilot (human pilot or an AI controller configured with AIpiloting code) that are somewhat different from corresponding outputfrom an actual drone. For example, real sensors and cameras may provideoutputs that include some noise. The absence of noise may not benoticeable to a human pilot, or a human pilot may easily compensate forany noise effects. This may not be the case for an AI controller. Inorder to more accurately simulate sensor and camera output, simulatedsensor noise may be added to simulated sensor outputs and simulatedcamera noise may be added to simulated camera outputs. While such noisemay be produced by a random number generator in some cases, noise thatmore accurately reflects real sensor/camera noise may be obtained fromtest bench testing of actual components (e.g. under static conditionswhere the noise may be easily isolated). Statistically significantpopulations of components may be tested to obtain noise recordings thatare then combined (e.g. averaged) to obtain simulated noise, which maybe added to simulated outputs prior to sending such outputs from asimulator to an AI controller.

FIG. 13 shows an example of a method of quadcopter simulation (e.g. insimulator 502) that includes recording camera output for one or morevideo cameras under constant conditions 1300 (e.g. with camera lenscovered), subtracting a constant signal from the recorded camera outputto obtain a camera noise recording 1302, and generating a simulatedcamera noise from the camera noise recording 1304. Camera noiserecordings from bench testing of a statistically significant number ofcameras (e.g. more than ten) may be combined to generate the simulatedcamera noise from combined camera noise recordings from multiplecameras. The method also includes adding the simulated camera noise to aplurality of simulated camera outputs of a quadcopter simulator togenerate a plurality of noise-added simulated camera outputs 1306, andsending the plurality of noise-added simulated camera outputs to anArtificial Intelligence (AI) controller coupled to the quadcoptersimulator for the AI controller to use to pilot a simulated quadcopterof the quadcopter simulator 1308. For example, camera view simulator1038 of FIG. 10A may add simulated camera noise to initial simulatedcamera outputs before sending the noise-added simulated camera outputsto AI controller 508.

FIG. 14 shows an example of a method of quadcopter simulation (e.g. insimulator 502), which may be used in combination with the method of FIG.13, or separately. The method includes recording sensor output for oneor more sensors under constant conditions 1400, subtracting a constantsignal from the recorded sensor output to obtain a sensor noiserecording 1402, generating a simulated sensor noise from the sensornoise recording 1404. Sensor noise recordings from bench testing of astatistically significant number of sensors (e.g. more than ten) overdifferent conditions may be combined to generate the simulated sensornoise. The method also includes adding the simulated sensor noise to asimulated sensor output of a quadcopter simulator to generate anoise-added simulated sensor output 1406 and sending the noise-addedsimulated sensor output to an Artificial Intelligence (AI) controllercoupled to the quadcopter simulator for the AI controller to use topilot the quadcopter simulator 1408. For example, sensor simulator 1037of FIG. 10A may add simulated sensor noise to initial simulated sensoroutputs before sending the noise-added simulated sensor outputs to AIcontroller 508.

FIG. 15 shows an example of an arrangement for performing the recordingof step 1400 of FIG. 14. A PC 1500 with suitable recording capability iscoupled to receive outputs from a camera 1502 and a sensor 1504. PC 1500may record outputs while camera 1502 and sensor 1504 are in conditionsto provide constant output. For example, camera 1502 may have its lenscovered (for constant zero light conditions) or may be pointed to anunchanging image. Sensor 1504 may be a gyroscope (e.g. in an IMU) andmay be spun at a constant speed (e.g. constant angular velocity aroundan axis) or may be an accelerometer that is at constant acceleration(e.g. stationary), or a rangefinder that is maintained a constantdistance from an object or surface. A constant signal may be subtractedfrom recorded outputs to obtain camera and sensor noise for multiplecameras (e.g. ten or more cameras) and sensors (e.g. ten or moresensors) and these may then be used to generate simulated camera noiseand simulated sensor noise to be added to simulated outputs of asimulator. Camera lens distortion, sensor error, and/or other effectsmay also be recorded during bench testing and may be incorporated into asimulated sensor and camera outputs.

Debugging

In order to ensure that AI code including participant code (e.g.participant code 520 operating in AI controller 508 of FIG. 5) isfunctional prior to deployment, debugging may be performed while AI codeoperates to pilot a simulated drone. For example, as shown in FIG. 6A,AI controller 508, running AI code, may be coupled to workstation 628that includes simulator 502 and debugger 629. FIG. 16 provides anexample implementation of such an arrangement that is suitable fordebugging and that may be supported by an IDE (e.g. as debuggingfeatures 996 of general IDE features 986 illustrated in FIG. 9).Workstation 628 includes simulator 502 and debugger 629, which may beGNU Debugger (GDB). Debugger 629 allows insertion of breakpoints in AIpiloting code (e.g. participant code 520) so that AI code stops atpredetermined locations. Debugger 629 can then generate a list of AIcode variable values of the AI piloting code at the breakpoint.Workstation 628 is coupled to AI controller 508 running participant code520 (and other code for piloting a simulated drone). AI controller alsoincludes gdbserver 1600, a program in AI controller 508 that works inconjunction with debugger 629 to debug participant code 520.Debugging-related communication between debugger 629 and gdbserver 1600may be through an Ethernet connection between workstation 628 and AIcontroller 508 and may use a private IP address range. In thisarrangement, simulator 502 is a quadcopter simulator configured toreceive quadcopter flight control commands and to generate simulatedsensor output and simulated camera output for a plurality ofstereoscopic cameras of a simulated quadcopter, the quadcopter simulatorimplemented workstation 628. AI controller 508 is coupled to theworkstation 628, the AI controller configured to receive the simulatedsensor output and the simulated camera output for the plurality ofstereoscopic cameras from the quadcopter simulator 502, determine aflightpath for the simulated quadcopter according to the simulatedsensor output and the simulated camera output, generate the quadcopterflight control commands according to the flightpath, and provide thequadcopter flight control commands to the quadcopter simulator 502.Debugger 629 is implemented in the workstation 628, the debugger iscoupled to the simulator 502 and the AI controller 508. The debugger 629is coupled to perform debugging of AI code of the AI controller 508(e.g. participant code 520) while the AI controller 508 generates thequadcopter flight control commands for the simulator 502.

AI controller 508 includes a first clock 1602 while workstation 1604includes a second clock 1604. These clocks may be synchronized to a highlevel of accuracy to facilitate debugging. In particular, when an erroroccurs in participant code 520 it may stop while simulator 502continues. Accurate synchronization allows simulator 502 to be rewoundto a state corresponding to the error, or to an earlier state, so thatoperations leading up to and including the error in simulator 502 andparticipant code 520 can be examined. Simulator operations (e.g.quadcopter simulator operations) may be recorded (logged) so tofacilitate stepping through operations to identify errors in participantcode 520. A high degree of accuracy may be achieved using Precision TimeProtocol (PTP), e.g. as detailed in IEEE 1588. Using PTP hardwaretimestamping may allow synchronization accuracy of less than 1millisecond. Communication between simulator 502, debugger 629,gdbserver 1600 and participant code 520 may be PTP hardware timestampedso that the last communication from participant code 520 prior to afailure can be used to identify the timing of the failure with a highdegree of accuracy and corresponding operations of simulator 502 may befound.

FIG. 17 illustrates an example of a method of debugging quadcopterpiloting code that may be implemented using hardware illustrated in FIG.16. The method includes coupling an Artificial Intelligence (AI)controller configured with AI piloting code to a workstation having aquadcopter simulator 1700 (e.g. coupling AI controller 508 toworkstation 628 in FIG. 16), initiating piloting of a simulatedquadcopter of the quadcopter simulator by the AI piloting code of the AIcontroller 1702, logging operations of the quadcopter simulator duringthe piloting of the simulated quadcopter 1704, and timestampingcommunication between the AI piloting code and the quadcopter simulator1706. The method includes, subsequently, in response to an AI pilotingcode event at an event time, determining the event time from atimestamped communication 1708, finding a logged operation of thequadcopter simulator having a timestamp corresponding to the event time1710, rewinding the quadcopter simulator to at least the loggedoperation 1712; and stepping through one or more operations of thequadcopter simulator and the AI piloting code to identify AI pilotingcode errors relating to the AI piloting code event 1714.

An AI controller may be implemented using various components, with FIG.18 providing an example of a controller module that includes aSystem-on-Chip (SOC) 1800 (e.g. NVIDIA Xavier SOC), which may includecore CPUs (e.g. 12 Volta core GPUs and 8 core ARM 64 bit CPUs), RAM(e.g. 16 GB LPDDR4 memory) and additional components. This may beconsidered an implementation of AI controller 506 of FIG. 6A and issimilarly shown coupled to workstation 628. HDMI to MIPI converter 634converts simulated camera output from workstation 628 to MIPI format forAI controller 508. AI controller 508 includes a fan 1804 and fan driver1802 to provide cooling as required (AI controller may be housed in anenclosure and may generate significant heat). A power adaptor 1806 andexternal power interface 1808 are provided to receive electrical powerfrom an external source (e.g. power supply or battery) and provide it tosubsystem power unit 1810. Subsystem power unit 1810 is also coupled tobattery 1812 and battery management unit 1814 and can select a powersource to power SOC 1800 and other components (e.g. using battery 1812when external power is not available).

In an alternative configuration, AI controller 508 may be coupled tolive hardware (e.g. live hardware 510, shown in FIG. 6A, for livehardware testing). FIG. 19 shows an example in which live hardware 510includes six video cameras 1900, for example, IMX265 MIPI cameras fromLeopard Imaging, Inc. Live hardware 510 also includes sensors 1902including IMUs 1904 and rangefinder 1906. IMUs 1904 may include twoIMUs, for example a Bosch 6 axis BMI088 and an InvSense 6 axis MPU-6050TDK. Rangefinder 1906 may be a Garmin LIDAR lite V3. The configurationof FIG. 19 may be used for hardware-in-the-loop testing of AI code of AIcontroller 508 after testing using a simulator as shown in FIG. 18.

When AI code is tested (e.g. using simulator and hardware-in-the-looptesting), the AI code may be deployed and used as an AI module of anautonomous drone to pilot the drone without remote-control input. FIG.20 shows an example of AI controller 508 in an autonomous drone 2000,which is configured to be piloted by AI controller 508. Autonomous drone2000 may be considered an example implementation of drone 301 of FIG. 3.Autonomous drone 2000 includes six video cameras 1900 (e.g. configuredas three stereoscopic cameras) and sensors 1902 including IMUs 1904 andrangefinder 1906. Output 2002 (e.g. SBUS output) from AI controller 508goes to an RF communications circuit 2004 (control radio module) whichis connected to antenna 2006 (which may couple it to a remote unit orremote-control). RF communications circuit 2004 is coupled to flightcontroller 2008 to send flight control commands to flight controller2008. Flight control commands may come from AI controller 508 or from aremote unit (via RF communications) according to the same command formatso that commands are interchangeable. Thus, when RF communicationscircuit 2004 receives a command from a remote unit to take over pilotingfrom AI controller 508, RF communications circuit 2004 stops sending thecommands from AI controller 508 to flight controller 2008 and insteadsends commands from the remote unit. Flight controller 2008 includesvarious modules including a motor controller 2010 module that controlsfour Electronic Speed Control (ESC) units 2012 that drive fourquadcopter motors 2014 (which are coupled to corresponding fixed-pitchpropellers—not shown). Hall effect sensors 2016 monitor quadcoptermotors 2014 to provide feedback to motor controller 2010. A transpondercontroller 2018 controls infrared (IR) emitters 2020 that may be used tomonitor a quadcopter as it flies around a racecourse. An LED controller2022 controls LEDs 2026 (Light Emitting Diodes) through multiplexer 2024(MUX) which may illuminate autonomous drone 2000. In addition to sensors1902 coupled to AI controller 508, sensors 2028 may be directlyconnected to flight controller 2008 and may include one or more IMUS anda barometer (e.g. Bosch BMP280). In addition to cameras 1900 coupled toAI controller 508 for computer vision, quadcopter 2000 includes camera2030, which may be used to send video to a remote user forremote-control piloting of quadcopter 2000 using first-person view(FPV). Output from camera 2030 is sent to On Screen Display unit 2032and to video transmitter 2034 for transmission to the remote user viaantenna 2036. A main battery 2040 provides a principal source of powerfor flight controller 2008 (including motors 2014) via flight controllersubsystem power unit 2042 and, in this example also provides power tobattery management unit 1814 of AI controller 508 (e.g. AI controller508 may be powered from main battery 2040 or from battery 1812).

An autonomous quadcopter such as autonomous quadcopter 2000 of FIG. 20may be operated so that it flies autonomously using an AI module such asAI controller 508 instead of a human pilot using a remote-control. Anexample of a method of operating a quadcopter is illustrated in FIG. 21which includes generating a plurality of stereoscopic camera views for aplurality of fields of view around the quadcopter 2100, providing theplurality of stereoscopic camera views to a hardware abstraction layer2102 (e.g. HAL 516), deinterleaving of the plurality of stereoscopiccamera views in the hardware abstraction layer 2104, providingdeinterleaved frames to an Artificial Intelligence (AI) module 2106, andgenerating object-location information for objects in the plurality offields of view from the deinterleaved frames in the AI module 2108. Forexample, objects around a drone racecourse may be located and identifiedusing computer vision based on stereoscopic camera views. The methodfurther includes determining in the AI module a flight path for thequadcopter according to the object-location information 2110, generatinga plurality of commands in the AI module corresponding to the flightpath 2112, sending the plurality of commands from the AI module to aflight controller of the quadcopter; 2114, and controlling a pluralityof motors of the quadcopter according to the plurality of commands suchthat the quadcopter follows the flight path 2116.

FIGS. 22A-D show an example of an implementation of autonomous drone2000 as shown schematically in FIG. 20, which may be operated asillustrated in FIG. 21. FIG. 22A shows autonomous drone 2000 from thefront with AI controller 508 mounted on top of chassis 2202 and withmotors 2204, 2206 and corresponding propellers 2208, 2210 also mountedon top of chassis 2202. Motors 2204, 2206 correspond to two of fourmotors 2014 of FIG. 20 (the other two motors are not visible in thisview. Six cameras are mounted on the bottom of chassis 2202. Cameras arearranged in pairs to form stereoscopic cameras. Thus, cameras 2212 a and2212 b form a first stereoscopic camera looking down and forward ofautonomous quadcopter 2000. Cameras 2214 a and 2214 b form a secondstereoscopic camera looking forward and to the right of autonomousquadcopter 2000 (to the left in the view of FIG. 22A). Cameras 2216 aand 2216 b form a third stereoscopic camera looking forward and to theleft of autonomous quadcopter 2000 (to the right in the view of FIG.22A).

FIG. 22B shows a perspective view of autonomous drone 2000 from aboveand to the left, showing propellers 2208, 2209, 2210, and 2211 mountedto the top of chassis 2202. AI controller 508 can be seen mounted to thetop of chassis 2202. Cameras 2214 b, 2216 a, and 2216 b, mounted to thebottom of chassis 2202 are also visible in this view.

FIG. 22C shows a top-down view of autonomous drone 2000 includingpropellers 2208, 2209, 2210, 2211 and AI controller 508 mounted on topof chassis 2202.

FIG. 22D shows a bottom-up view of autonomous drone 2000 includingcameras 2212 a, 2212 b, 2214 a, 2214 b, 2216 a, 2216 b mounted to theunderside of chassis 2202.

For purposes of this document, it should be noted that the dimensions ofthe various features depicted in the figures may not necessarily bedrawn to scale.

For purposes of this document, reference in the specification to “anembodiment,” “one embodiment,” “some embodiments,” or “anotherembodiment” may be used to describe different embodiments or the sameembodiment.

For purposes of this document, a connection may be a direct connectionor an indirect connection (e.g., via one or more other parts). In somecases, when an element is referred to as being connected or coupled toanother element, the element may be directly connected to the otherelement or indirectly connected to the other element via interveningelements. When an element is referred to as being directly connected toanother element, then there are no intervening elements between theelement and the other element. Two devices are “in communication” ifthey are directly or indirectly connected so that they can communicateelectronic signals between them.

For purposes of this document, the term “based on” may be read as “basedat least in part on.”

For purposes of this document, without additional context, use ofnumerical terms such as a “first” object, a “second” object, and a“third” object may not imply an ordering of objects, but may instead beused for identification purposes to identify different objects.

For purposes of this document, the term “set” of objects may refer to a“set” of one or more of the objects.

The foregoing detailed description has been presented for purposes ofillustration and description. It is not intended to be exhaustive or tolimit to the precise form disclosed. Many modifications and variationsare possible in light of the above teaching. The described embodimentswere chosen in order to best explain the principles of the proposedtechnology and its practical application, to thereby enable othersskilled in the art to best utilize it in various embodiments and withvarious modifications as are suited to the particular use contemplated.It is intended that the scope be defined by the claims appended hereto.

1. A method of simulating a quadcopter comprising: recording cameraoutput for one or more video cameras under constant conditions;subtracting a constant signal from the recorded camera output to obtaina camera noise recording; generating a simulated camera noise from thecamera noise recording; adding the simulated camera noise to a pluralityof simulated camera outputs of a quadcopter simulator to generate aplurality of noise-added simulated camera outputs; and sending theplurality of noise-added simulated camera outputs to an ArtificialIntelligence (AI) controller coupled to the quadcopter simulator for theAI controller to use to pilot a simulated quadcopter of the quadcoptersimulator.
 2. The method of claim 1 wherein the one or more videocameras include a statistically significant population of video camerasincluding at least ten cameras, and further comprising generating thesimulated camera noise from combined camera noise recordings of thepopulation of video cameras.
 3. The method of claim 1 wherein theplurality of simulated camera outputs includes six simulated cameraoutputs forming three stereoscopic camera outputs representing differentstereoscopic views of a simulated environment around the simulatedquadcopter.
 4. The method of claim 3 further comprising the AIcontroller providing the three stereoscopic camera outputs to a computervision unit and selecting a flightpath for the simulated quadcopterusing object identification and location information from the computervision unit.
 5. The method of claim 1 wherein recording camera outputfor one or more video cameras under constant conditions includescovering a camera lens to create constant zero light conditions.
 6. Themethod of claim 1 further comprising: recording sensor output for one ormore sensors under constant conditions; subtracting a constant signalfrom the recorded sensor output to obtain a sensor noise recording;generating a simulated sensor noise from the sensor noise recording;adding the simulated sensor noise to a plurality of simulated sensoroutputs of the quadcopter simulator to generate a plurality ofnoise-added simulated sensor outputs; and sending the plurality ofnoise-added simulated sensor outputs to the Artificial Intelligence (AI)controller coupled to the quadcopter simulator for the AI controller touse to pilot the simulated quadcopter of the quadcopter simulator. 7.The method of claim 6 wherein the one or more sensors include at leastan Inertial Measurement Unit (IMU) sensor and a rangefinder sensor. 8.The method of claim 1 further comprising: using the noise-addedsimulated camera outputs in a machine learning training set to teach AIcode of the AI controller to pilot a quadcopter; and subsequentlydeploying the AI code to an AI controller of a quadcopter to pilot thequadcopter.
 9. A method of simulating a quadcopter comprising: recordingsensor output for one or more sensors under constant conditions;subtracting a constant signal from the recorded sensor output to obtaina sensor noise recording; generating a simulated sensor noise from thesensor noise recording; adding the simulated sensor noise to a simulatedsensor output of a quadcopter simulator to generate a noise-addedsimulated sensor output; and sending the noise-added simulated sensoroutput to an Artificial Intelligence (AI) controller coupled to thequadcopter simulator for the AI controller to use to pilot thequadcopter simulator.
 10. The method of claim 9 wherein recording thesensor output is performed for a plurality of sensors over differentconditions and the simulated sensor noise is obtained from combinedsensor noise of the plurality of sensors.
 11. The method of claim 9wherein the one or more sensors include at least an Inertial MeasurementUnit (IMU) sensor and a rangefinder sensor.
 12. The method of claim 11wherein recording sensor output for an IMU sensor includes spinning theIMU sensor at a plurality of constant speeds to obtain a sensor noiserecording under different conditions.
 13. The method of claim 11 whereinrecording sensor output for a rangefinder sensor includes maintainingthe rangefinder sensor at a constant distance from an object.
 14. Themethod of claim 9 further comprising: recording camera output for one ormore video cameras under constant conditions; subtracting a constantsignal from the recorded camera output to obtain a camera noiserecording; generating a simulated camera noise from the camera noiserecording; adding the simulated camera noise to a plurality of simulatedstereoscopic camera outputs of a quadcopter simulator to generate aplurality of noise-added simulated stereoscopic camera outputs; andsending the plurality of noise-added simulated stereoscopic cameraoutputs to the Artificial Intelligence (AI) controller coupled to thequadcopter simulator for the AI controller to use in combination withthe noise-added simulated sensor output to pilot a simulated quadcopterof the quadcopter simulator.
 15. The method of claim 14 furthercomprising: using the noise-added stereoscopic simulated camera outputsand the noise-added simulated sensor outputs in a machine learningtraining set to teach AI code of the AI controller to pilot aquadcopter; and subsequently deploying the AI code to an AI controllerof a quadcopter to pilot the quadcopter.
 16. An apparatus comprising: anArtificial Intelligence (AI) controller configured to receive simulatedsensor output and simulated camera output, determine a flightpath for asimulated quadcopter according to the simulated sensor output and thesimulated camera output, and generate quadcopter flight control commandsaccording to the flightpath; and a quadcopter simulator configured toreceive the quadcopter flight control commands from the AI controller,generate initial simulated sensor output and initial simulated cameraoutput for a plurality of stereoscopic cameras of the simulatedquadcopter, add simulated sensor noise to the initial simulated sensoroutput to obtain noise-added simulated sensor output, add simulatedcamera noise to the initial simulated camera output to obtainnoise-added simulated sensor output, and send the noise-added simulatedsensor output and the noise-added simulated camera output to the AIcontroller to determine the flightpath.
 17. The apparatus of claim 16wherein the simulated sensor noise is obtained from testing of aplurality of sensors and the simulated camera noise is obtained fromtesting of a plurality of cameras.
 18. The apparatus of claim 16 whereinthe simulated sensor output includes output of simulated gyroscope, asimulated accelerometer, and a simulated rangefinder.
 19. The apparatusof claim 16 wherein the simulated camera output includes outputs of sixsimulated cameras forming three simulated stereoscopic cameras.
 20. Theapparatus of claim 16 wherein the quadcopter simulator is implemented ina workstation, the apparatus further comprising: an Ethernet connectionbetween the AI controller and the workstation to carry the noise-addedsimulated sensor output and the quadcopter flight control commands; HighDefinition Multimedia Interface (HDMI) outputs from the workstation tocarry the noise-added simulated camera outputs; one or more HDMI toMobile Industry Processor Interface (MIPI) converters to convert thenoise-added simulated camera outputs to MIPI format; and MIPI inputs tothe AI controller to carry MIPI format noise-added simulated cameraoutputs.